Point clouds have been extensively used in many applications including for example, object reconstruction, and object recognition. A common use for point clouds involves gathering data using Light Detection and Ranging (LiDAR) point clouds such as, street side view point LiDAR data, which have become a very informative data source for the on-board recognition of an autonomous vehicle. Such recognition may be based on matching incoming data with pre-processed data stored in a repository or memory.
Most techniques simplify point clouds by either randomly simplifying the point cloud or reconstructing the point cloud to a mesh first and simplifying the point cloud based on mesh. One technique that can work on a point cloud directly is called furthest point cloud sampling. However, this technique does not take into account local density of the point cloud, thus resulting in an almost uniform sampling in the simplification.
Simplification of point cloud data, such as uniform simplification or random sampling, changes the appearance of the original data to some extent, which causes information loss. This information loss may lead to the simplified point cloud not sharing the same characteristics as the original point cloud.